Implementation
of back propagation using neuralnet package
The
backward propagation of errors or back-propgation is a common method of training artificial
neural networks used in conjunction with an optimization method such as gradient
descent.
The
algorithm repeats a two phase cycle propagation and weight update.
When
an input vector is presented to the network , it is propgated forward through
the network layer by layer.
The
goal of back-propgation is to optimize the weights so that the neural network
can learn how to correctly map arbitrary inputs to outputs.
Back-propgation
is an algorithm for supervised learning of artificial neural networks using
gradient descent.
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